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Found 17 papers, 7 papers with code
Generating Programmatic Referring Expressions via Program Synthesis
The policy neural network employs a program interpreter that provides immediate feedback on the consequences of the decisions made by the policy, and also takes into account the uncertainty in the symbolic representation of the image.
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Finding errors in machine learning applications requires a thorough exploration of their behavior over data.
Rectifying Group Irregularities in Explanations for Distribution Shift
It is well-known that real-world changes constituting distribution shift adversely affect model performance.
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning.
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision
We propose LASER, a neuro-symbolic approach to learn semantic video representations that capture rich spatial and temporal properties in video data by leveraging high-level logic specifications.
Scallop: A Language for Neurosymbolic Programming
We present Scallop, a language which combines the benefits of deep learning and logical reasoning.
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Test-time adaptation reduces these violations by up to 68. 7% with relative performance improvement up to 32%.
Learning to Select Pivotal Samples for Meta Re-weighting
In this paper, we study how to learn to identify such a meta sample set from a large, imperfect training set, that is subsequently cleaned and used to optimize performance in the meta re-weighting setting.
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning
Deep learning and symbolic reasoning are complementary techniques for an intelligent system.
Numerical Reasoning over Legal Contracts via Relational Database
Numerical reasoning over text requires deep integration between the semantic understanding of the natural language context and the mathematical calculation of the symbolic terms.
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
Designing a suitable representation for code-reasoning tasks is challenging in aspects such as the kinds of program information to model, how to combine them, and how much context to consider.
HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS
We present a learning-based approach to detect and fix a broad range of bugs in Javascript programs.
Synthesizing Datalog Programs Using Numerical Relaxation
The problem of learning logical rules from examples arises in diverse fields, including program synthesis, logic programming, and machine learning.
Learning a Meta-Solver for Syntax-Guided Program Synthesis
Our framework consists of three components: 1) an encoder, which embeds both the logical specification and grammar at the same time using a graph neural network; 2) a grammar adaptive policy network which enables learning a transferable policy; and 3) a reinforcement learning algorithm that jointly trains the specification and grammar embedding and adaptive policy.
Learning Neurosymbolic Generative Models via Program Synthesis
Significant strides have been made toward designing better generative models in recent years.
Learning Loop Invariants for Program Verification
A fundamental problem in program verification concerns inferring loop invariants.
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
Our two SPORE algorithms are able to build relationships between responses (e. g., the execution time of a computer program) and features, and select a few from hundreds of the retrieved features to construct an explicitly sparse and non-linear model to predict the response variable.
